{
  "abstract": "Collaborative autonomous driving utilizing Federated Learning (FL) is frequently constrained by long-tailed data distributions and perceptual imbalances. Implementing federated learning for environmental scene classification in autonomous driving faces severe challenges due to the long-tailed and non-IID distribution of real-world climatic data. To address this, this paper proposes FedRare, a client selection framework based on a multi-dimensional utility function, integrating scenario criticality ( $$W_C$$ ), distribution rarity ( $$W_R$$ ), and local update quality ( $$W_L$$ ). To address the safety concerns in edge cases, we implement a granular evaluation protocol by categorizing 18 driving scenarios into standard, diverse, and safety-critical groups. Experiments on the BDD100K dataset show that FedRare achieves a mean recall of 72.82% during the late training stage (averaged over the final five communication rounds across five random seeds) while the baseline is 63.42%. Results from the trained model validation demonstrate that FedRare achieves a 74.13% recall in safety-critical groups (e.g., rainy/snowy night), while maintaining high perceptual reliability where traditional loss-based methods often fail. Furthermore, the $$W_L$$ constraint stabilizes the training process, keeping the performance fluctuation (standard deviation) at approximately 0.02. This work proposes a framework to enhance perceptual robustness for autonomous driving within complex, long-tailed environments.",
  "abstract_shown": true,
  "affiliations": [
    "School of Artificial Intelligence, HuBei Open University",
    "School of Computer Engineering, HuBei University of Arts and Science"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Song Wen"
    },
    {
      "affs": [
        1
      ],
      "name": "Xinying Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9y0ef332w3rpk.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-62562-8",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-62562-8",
  "id": "9y0ef332w3rpk",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-62562-8",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1702|Privacy-Preserving Technologies in Data"
    ]
  ],
  "openalex": "W7169581848",
  "pdf_url": "https://www.nature.com/articles/s41598-026-62562-8_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/9y0ef332w3rpk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:53Z",
      "native_id": "6017d9be09394ec7b4681e266887d659",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-62562-8"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "An adaptive client selection method for long tailed scene classification in autonomous driving",
  "title_text": "An adaptive client selection method for long tailed scene classification in autonomous driving",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:53Z",
  "url": "https://research.draconitic.com/paper/9y0ef332w3rpk",
  "venue": "Scientific Reports",
  "versions": []
}